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Full Length Article | Open Access

Battery SOH estimation method based on gradual decreasing current, double correlation analysis and GRU

Chaolong Zhanga,b(), Laijin Luoc(), Zhong Yanga, Shaishai Zhaob, Yigang Heb, Xiao Wangb, Hongxia Wangd
College of Intelligent Science and Control Engineering, Jinling Institute of Technology, Nanjing, China
School of Electrical and Automation, Wuhan University, Wuhan 430072, China
School of Electronic Engineering and Intelligent Manufacturing, Anqing Normal University, Anqing, China
Electrical and Computer Engineering, University of Denver, CO, United States
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HIGHLIGHTS

· Gradual decreasing current in the CV charging phase of battery is measured as raw data to extract features. The method is appropriate because the CV charging phase is always present in CC-CV phase of battery charging of EVs no matter the initial SOCs of charged batteries.

· A double correlation analysis is proposed to calculate the grey correlation degree between each feature and battery SOH, and then choose the primary feature and four secondary features. The primary feature is used to calculate the Pearson correlation degree with the other four secondary features to select the complementary features to consist combined features.

· The learning rate of GRU is optimized by the SSA, and the optimized GRU is applied to set up a SOH estimation model.

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Abstract

In intelligent lithium-ion battery management, the state of health (SOH) of battery is essential for the batteries’ running in electric vehicles. Popularly, the battery SOH is estimated by using suitable features and data-driven methods. However, it is difficult to extract appropriate features characterizing battery SOH from the charging and discharging data of batteries owing to various state of charges (SOCs) and working conditions of batteries. In order to effectively estimate the battery SOH, an estimation method based on gradual decreasing current, double correlation analysis and gated recurrent unit (GRU) is proposed in this paper. Firstly, gradual decreasing current in the constant voltage charging phase is measured as the raw data. Then, the double correlation analysis method is proposed to select combined features characterizing the battery SOH from different categories of features. Meanwhile, the number of input features is also ensured by the method. Finally, the GRU algorithm is employed to set up a SOH estimation model whose learning rate is improved by using a sparrow search algorithm (SSA) for the purpose of capturing the hidden relationship between features and SOH. The adaptability of the proposed method is validated by SOH estimation experiments of a single battery and a battery pack. Additionally, contrast experiments are performed to show the advanced estimation performance of the proposed method.

Keywords

Lithium-ion batteryState of healthGradual decreasing currentCombined featuresDouble correlation analysisGated recurrent unitSparrow search algorithm

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Green Energy and Intelligent Transportation
Volume 2 Issue 5,
October 2023
DOI: 10.1016/j.geits.2023.100108
Cite this article:
Zhang C, Luo L, Yang Z, et al. Battery SOH estimation method based on gradual decreasing current, double correlation analysis and GRU. Green Energy and Intelligent Transportation, 2023, 2(5). https://doi.org/10.1016/j.geits.2023.100108
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